Huntington's disease (HD) and Huntington's disease-like 2 (HDL2) are remarkably similar autosomal dominant adult onset neurodegenerative disorders, nearly indistinguishable clinically and pathologically. Each disease is ultimately fatal with no effective treatment to stop or slow the relentless progression. HD affects about 30,000 Americans, with a much higher number at risk; HDL2 is rare. The complete explanation for HD and HDL2 pathogenesis remains elusive. A novel strategy for focusing the search for disease mechanisms and therapeutic targets of HD is to determine those points at which the pathogenic pathways of HD and HDL2 converge. A particularly powerful method for implementing this strategy is to compare the transcriptomes of the two diseases. Based on our preliminary evidence, we hypothesize that both abnormal levels of gene expression and abnormal gene splicing will be present in HD and HDL2 and that the sets of these abnormalities will overlap in the two diseases. Here we propose to take advantage of the remarkable similarities of HD and HDL2 to identify convergent pathogenic pathways, via parallel transcriptome characterization of mouse models and human patient samples of HD and HDL2. We propose two specific aims. In aim 1, we will use state of the art exon junction array, RNA sequencing (RNA-Seq), and analytic methods to examine and compare RNA extracted from human HD and HDL2 postmortem brains and mouse models of HD and HDL2 as well as controls. In aim 2, we will experimentally validate expression and splicing abnormalities using high-throughput automated PCR assays and new RNA samples, compare mouse and human data, and use bioinformatics tools to determine common gene sets, pathways, and molecular subnetworks shared by genes showing gene expression or splicing abnormalities in HD and HDL2 brains. We anticipate that the proposed studies will create an extremely valuable resource that will provide a detailed characterization of the HD and HDL2 transcriptomes at an unprecedented resolution and hence fundamentally improve understanding of disease pathophysiology.